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At the Craig Newmark Graduate School of Journalism at the City University of New York, change is in our DNA. That comes of being born in 2006, as the digital revolution was transforming our profession in ways none of us could have imagined.
This 15-week, 3-credit advanced course trains students to gather and analyze complex data to uncover stories and bullet-proof their nut grafs. Students will learn Python — one of the most popular newsroom languages (and data science languages in general). They will use Python libraries like Pandas, Matplotlib and BeautifulSoup to acquire, process, query and summarize millions of rows of data. Students will also learn more advanced statistics that will allow them to find story nuggets in trickier data while also avoiding statistical pitfalls. They will also tap into the latest data analysis techniques, like applying natural language processing to make sense of unstructured information and to find bots masquerading as humans. Students who successfully complete this course will gain a high level of confidence in their data journalism abilities.
Madeleine Varner is an investigative data journalist at The Markup.
Previously, she was a researcher at ProPublica, where she was on a team that won a Loeb Award for Beat Reporting in 2017 for “Monetizing Hate,” a series of stories that examined Facebook’s ad practices.
● CRN: 66929
● Credits: 3 credits
● Semester: Spring 2020
● Duration: 15 weeks (January 30 - May 21)
● When/where: Wednesdays 5:30 pm to 8:20 pm in Room 432
● Instructors: Lam Thuy Vo, Madeleine Varner
Communications channels and office hours:
● Google Classroom: You will receive feedback, assignments and other information at your Google Classroom
Data sets are everywhere. In public sources, like election results, budgets and census reports; semi-public and private datasets, like hidden company information; in cross referencing people and organizations in documents and databases to discover conflicts of interest; in social media updates, images and video uploads. Data has become an invaluable resource for journalists to expose stories buried in the numbers and find relevant facts to shape them in newsworthy ways to produce great stories. And today, no matter if your goal is to cover a daily beat or to do enterprise or investigative stories, you are expected to be able to use it.
In this course you will learn the fundamental skills you need to do data journalism:
- Data journalism history and principles.
- How to find and acquire data, as well as how to negotiate access to data with officials by using FOIA/FOIL.
- Work with common data formats and different types of data, as well as to understand what sort of data are in rows and columns.
- Discover how to spot errors, deal with missing values and messy data.
- How to clean data, normalize it, analyze it and test your results using basic math, statistics and data journalism tools
- To mix data skills with on-the ground reporting to be able to discover newsworthy stories in data and answer questions to do accountability journalism that serves the public interest.
Most importantly, we want to focus on getting you the skills you need to find stories in data and be able to come to your editor with data-driven pitches.
How the class works
This is a hands-on course. Each lesson will focus on one or two of our expected outcomes, moving sequentially through the course. Lessons will include:
- Lectures, discussions and updates on your reporting
- Lab time to work and practice with real datasets and use computer-assisted tools and basic programming (Google Sheets, Open Refine, command line, Python with Jupyter Notebooks) to obtain, clean, normalize, analyze and bulletproof data.
You will be expected to conduct the following work outside of the classroom:
- Homework exercises
- Readings
- 5 story pitches.
All the course materials will be shared by the instructors with the students on a Github and on Google Classroom.
Tools
Some software is already installed on your laptop. Others, you will have to install on your own. The instructors will do an “install party” for this.
- Python: includes Jupyter Notebooks, data analysis packages, package management tools, and environment manager to create virtual environments.
Objective and outcomes
The objective of this course is to train students in the fundamental skills to do data journalism and be ready to continue their training in the Advanced Data Journalism course.
At the end of this course you will be expected to be able to:
- Understand the principles and process of doing data journalism.
- Do online and offline research to obtain documents and data.
- Understand and use public record laws to negotiate access to data.
- Know the characteristics of different file formats and types of data
- Check quality of data to identify errors, missing values and how to solve this issues.
- Use basic math and descriptive and inferential statistics for data analysis.
- Organize, explore, clean and do accurate solid analysis of different types of data by using the tools of data journalism (Google Sheets, Open Refine, command line, Python with packages and Jupyter Notebooks ).
- Ask interesting and answerable questions of data.
- Maintain data integrity and use best practices in data journalism for reproducibility methods.
- Combine data work with fact checking, interviewing sources and on-the ground reporting to produce quality journalism.
- Evaluate professional data stories (what makes a particular project successful or not?).
Class Schedule
Schedule is subject to be changed by instructors, depending on how well you are progressing. Any modifications will be announced by the instructors.
1.1 — Week 1 (Jan. 30)
What’s this course about, data journalism fundamentals
Interactive lesson on data assignments: Students will come up with a news story based on the data headers they see based on the rubrics of the previous week
Refine and submit your assignment pitch by Friday May 3
2.6 — Week 14 (May 6)
**Geospatial data analysis with Geopandas
**
Discussion: Ethan presents an interesting data story or data set for the class to discuss
Lecture: Pick one of the past four pitches for a lightning talk at the end of next week. Refine them in class!
Hands-on: Bigfoot sightings exercise
Final class! Week 15 (May 13)
Pitch day, how did we all do?
Objective: Present your best pitch. Evaluate the course and instructors evaluation.
- Presentations
- Class evaluations
Class rules
Students-Instructors contract: The success of this course depends on the level of commitment of each student. That is, it is up to each student to carry out their class work and assignments as well as to contribute to their team’s reporting project and speak up about any doubts or concerns you may have. In return, the instructors will do their best to provide a clear lesson plan, give students timely feedback and advice them to achieve the course expected outcomes.
Attendance and punctuality: We meet 15 times during the spring semester. You must attend every class and be on time. If you’re sick or you have an emergency, let us know via Slack or text. If you don’t show up, you will hear from us. More than one unexcused absence will penalize your overall grade by 5%. Similarly, two tardiness equals an absence.
Deadlines matter. This is of vital importance, not only for the class but also in the professional career of any journalist, because deadlines are sacred. Please carefully note these rule: there will be a 10 percent deduction of your assignment grade for every 24 hours that passes after a deadline in which you have not turned it in. No exceptions. Except medical emergency or family emergency. Make-up work will not be offered except in extenuating circumstances.
Communicate. If you have a problem or if you have difficulties, tell us right away, not after is too late. In journalism that’s what we do. When we have a problem we immediately tell our editor.
Be accurate and use language correctly. The value of journalistic work depends on credibility. That is why class assignments must have a rigorous verification of the data and information presented. That is the basis of the profession. A story with erroneous information can carry out an F grade. We expect the language to be used correctly. Follow the AP Style guidelines. Aim for clarity, precision and correct spelling and grammar.
Keep up with the news. Consuming information on a daily basis leads to a healthy diet of background, helps you connect the dots and discover story ideas to work on. If you care about a topic or your subject concentration you have to stay in top of the game.
Be a pro. Honesty, courtesy, curiosity and professionalism are the core values of a journalist. Behave like one because you are a journalist. When classmates are presenting or we have guests or we are working in teams don’t multitask, focus.
Participation. It is important to maintain an attitude of openness. Class time is reserved for learning and discussing the topics of each session. It is not the time for personal calls, text messages, emails and social networks.
Diversity and inclusion. It's critical that students learn to include a diverse set of voices in their stories, something that is often glossed over when finding stories in spreadsheets and online sources. You are encouraged and expected to look for stories about and voices from communities that are underrepresented. This also applies to our classroom. It requires us all to discuss differences with respect and empathy, regarding race, gender, age, religion, sexual preference, disability, language, origin or political beliefs.
Code of honor. This class follows the guidelines of the Student Handbook of our school. More so, in journalism plagiarism or falsification of data, sources and facts are serious crimes that can lead to failing this class. You may also be the subject of suspension, probation or expulsion, pending the decision of the School administration.
Assignments and Due Dates
4 Story pitches: 80 points.
Each pitch should be 1 page long, 2 pages long max. and including the following things:
● Byline
● What is your story about? Tell us in 1 headline and 1 lead paragraph. This should include answers to the following questions:
○ Why this story is relevant ("So what?) and why now?
○ What is the single question your story tries to answer?
○ Why will this story resonate with your audience?
○ What else has been done on this topic? (Provide links) and how is your angle different or fresh?
● Show us your data work! Give us access to a Google Sheet or a Jupyter Notebook!
● Write up at least one or up to three findings from your analysis based on the dataset that was given
● Maximum/minimum.
○ What is the maximum (best) story possible?
○ What's the minimum (fallback) story if your hypothesis doesn't prove out?
Class participation, readings and homework: 20 points
We expect you to participate in the class and pay respect to each other. This means partaking in discussions, in the hands-on drills and other class activities as well as completing homework.
The 8 homework assignments are individual drills to to evaluate your understanding of the material taught in classes.
- Total: 100 points.
How to file assignments
Each student will create a personal Drive homework folder name-lastname-homework to save his/her homework. Each team will create a team folder team-lastname-lastname-lastname to file teamwork. After you create your personal and team folder, share them with your instructors with editing permission.
All assignments are filed by class day at noon, by saving them to your drive folder (individual homework folder, and team folder) and by recording that you filed in the assignments form:http://bit.ly/fileheredj18
Breakdownof due dates
Details for each homework assignment will be posted in the shared Google Classroom. Details of the requirements for team reporting assignments are already posted in Google Classroom and will be explained in the first class.
Due date (11:59PM)
Assignment or homework that is due
February 15
Homework 1 : Import, clean and organize data with Google Sheets
February 22
Homework 2: Math quiz with Google Sheets
March 1
Homework 3: Visualizations in Sheets
March 8
Homework 4: Clean data with Open Refine
March 23
Homework 5: FOIA request
March 29
Homework 6: Python Exercise
April 5
Assignment 1: Story Pitch Homework 7: Python Jupyter Notebooks quiz
April 12
Assignment 2: Story Pitch Homework 8: Python Jupyter Notebooks data analysis
April 19
Assignment 3: Story Pitch
May 3
Assignment 4: Story Pitch
May 15
Presentations
Grading
Rubric
The story pitchesmakes up 80**% of your grade, each 20%. Graded as:**
4 Pitches: 80 points altogether, 20 points each
Each pitch
20 pts. On time + meets all project criteria + original reporting + effective use of data
17 pts. On time + meets most if not all of project criteria + acceptable reporting + acceptable use of data
13 pts. On time + meets very little of project criteria + somewhat acceptable reporting + somewhat acceptable use of data
10 pts. Late and/or meets little of project criteria + weak reporting + weak use of data
7 pts. Late and/or does not mean project criteria + very weak reporting + very weak use of data
3 pts. Late and/or shows little to no effort
0 pts. Not submitted within 1 week of deadline
8 Homeworks: 16 points
Each homework
2 pts. Completed homework.
1 pts. Completed partially. If this happens, your instructor will leave you a short comment to help you complete the exercise. If you do it, you’ll get full points.
0 pts. Not submitted
Class participation and readings discussion. 4 points.
4 pts. Amazing participation, asks questions, comments readings, shares ideas, works well with others
3 pts. Most of the time participates, asks questions, shares ideas, works well with others.
2 pts. Could do better
1 pts. Not engaged most of the times
0 pts. Not engaged at all
Total points: 100 = 100%
Scale
Final course grades, according to the grading scale used in the CUNY Graduate School of Journalism:
● 97: A+ Stellar work. Ready to be published by a professional news organization with minimal changes.
● 93: A Excellent work. It is ready to be published professionally with some changes.
● 90: A- Good quality work, although it needs a slightly more significant revision to be able to be published.
● 87: B+ Solid work that shows some deficiencies that need to be solved.
● 83: B Meets certain requirements, but lacks several important elements.
● 80: B- Below average and needs strong overall improvements.
● 77: C+ Poor job. It presents many problems of structure, reporting and storytelling
● 73: C Almost unacceptable because of major overall problems.
● 70: C- Unacceptable. Does not meet the minimum requirements of a graduate level journalism project.
● Anything below a 70 is an F. Work has failed at every level. There are no D in CUNY’s grading scale.
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Data Journalism - JOUR73351
At the Craig Newmark Graduate School of Journalism at the City University of New York, change is in our DNA. That comes of being born in 2006, as the digital revolution was transforming our profession in ways none of us could have imagined.
This 15-week, 3-credit advanced course trains students to gather and analyze complex data to uncover stories and bullet-proof their nut grafs. Students will learn Python — one of the most popular newsroom languages (and data science languages in general). They will use Python libraries like Pandas, Matplotlib and BeautifulSoup to acquire, process, query and summarize millions of rows of data. Students will also learn more advanced statistics that will allow them to find story nuggets in trickier data while also avoiding statistical pitfalls. They will also tap into the latest data analysis techniques, like applying natural language processing to make sense of unstructured information and to find bots masquerading as humans. Students who successfully complete this course will gain a high level of confidence in their data journalism abilities.
Lam Thuy Vo
https://www.journalism.cuny.edu/2021/03/lam-thuy-vo-named-to-new-role-to-strengthen-data-journalism-curriculum/
Madeleine Varner is an investigative data journalist at The Markup.
Previously, she was a researcher at ProPublica, where she was on a team that won a Loeb Award for Beat Reporting in 2017 for “Monetizing Hate,” a series of stories that examined Facebook’s ad practices.
● CRN: 66929
● Credits: 3 credits
● Semester: Spring 2020
● Duration: 15 weeks (January 30 - May 21)
● When/where: Wednesdays 5:30 pm to 8:20 pm in Room 432
● Instructors: Lam Thuy Vo, Madeleine Varner
Communications channels and office hours:
● Google Classroom: You will receive feedback, assignments and other information at your Google Classroom
● Email for specific individual questions (lam.vo@journalism.cuny.edu, madeleine.varner@journalism.cuny.edu)
● Text messages: for urgent matters only (Lam Vo: 347-977-3784, Madeleine Varner: 216-288-3771)
● Office hours by appointment: request via email.
**TABLE OF CONTENTS
**
Course Description
How the class works
Tools
Objective and outcomes
Class Schedule
1.1 — Week 1 (Jan. 30)
1.2 — Week 2 (Feb. 6)
1.3 — Week 3 (Feb. 13)
1.4 — Week 4 (Feb. 20)
1.5— Week 5 (Feb. 27)
1.6 — Week 6 (March 6)
1.7 — Week 7 (March 13)
1.8 — Week 8 (March 20)
2.1 — Week 9 (March 27)
2.2 — Week 10 (April 3)
2.3 — Week 11 (April 10)
2.4 — Week 12 (April 17)
2.5— Week 13 (May 1)
2.6 — Week 14 (May 8)
Final class! Week 15 (May 15)
Class rules
Assignments and Due Dates
Grading
Rubric
Scale
Further reading
Coaches
** Course Description **
Data sets are everywhere. In public sources, like election results, budgets and census reports; semi-public and private datasets, like hidden company information; in cross referencing people and organizations in documents and databases to discover conflicts of interest; in social media updates, images and video uploads. Data has become an invaluable resource for journalists to expose stories buried in the numbers and find relevant facts to shape them in newsworthy ways to produce great stories. And today, no matter if your goal is to cover a daily beat or to do enterprise or investigative stories, you are expected to be able to use it.
In this course you will learn the fundamental skills you need to do data journalism:
- Data journalism history and principles.
- How to find and acquire data, as well as how to negotiate access to data with officials by using FOIA/FOIL.
- Work with common data formats and different types of data, as well as to understand what sort of data are in rows and columns.
- Discover how to spot errors, deal with missing values and messy data.
- How to clean data, normalize it, analyze it and test your results using basic math, statistics and data journalism tools
- To mix data skills with on-the ground reporting to be able to discover newsworthy stories in data and answer questions to do accountability journalism that serves the public interest.
Most importantly, we want to focus on getting you the skills you need to find stories in data and be able to come to your editor with data-driven pitches.
How the class works
This is a hands-on course. Each lesson will focus on one or two of our expected outcomes, moving sequentially through the course. Lessons will include:
- Lectures, discussions and updates on your reporting
- Lab time to work and practice with real datasets and use computer-assisted tools and basic programming (Google Sheets, Open Refine, command line, Python with Jupyter Notebooks) to obtain, clean, normalize, analyze and bulletproof data.
You will be expected to conduct the following work outside of the classroom:
- Homework exercises
- Readings
- 5 story pitches.
All the course materials will be shared by the instructors with the students on a Github and on Google Classroom.
Tools
Some software is already installed on your laptop. Others, you will have to install on your own. The instructors will do an “install party” for this.
- Google Sheets
- Google Drive
- Text editor Atom or Sublime Text
- Personal account in Github
- Open Refine and Tabula
- Command line Terminal (already in your laptop)
- Python: includes Jupyter Notebooks, data analysis packages, package management tools, and environment manager to create virtual environments.
Objective and outcomes
The objective of this course is to train students in the fundamental skills to do data journalism and be ready to continue their training in the Advanced Data Journalism course.
At the end of this course you will be expected to be able to:
- Understand the principles and process of doing data journalism.
- Do online and offline research to obtain documents and data.
- Understand and use public record laws to negotiate access to data.
- Know the characteristics of different file formats and types of data
- Check quality of data to identify errors, missing values and how to solve this issues.
- Use basic math and descriptive and inferential statistics for data analysis.
- Organize, explore, clean and do accurate solid analysis of different types of data by using the tools of data journalism (Google Sheets, Open Refine, command line, Python with packages and Jupyter Notebooks ).
- Ask interesting and answerable questions of data.
- Maintain data integrity and use best practices in data journalism for reproducibility methods.
- Combine data work with fact checking, interviewing sources and on-the ground reporting to produce quality journalism.
- Evaluate professional data stories (what makes a particular project successful or not?).
Class Schedule
Schedule is subject to be changed by instructors, depending on how well you are progressing. Any modifications will be announced by the instructors.
1.1 — Week 1 (Jan. 30)
What’s this course about, data journalism fundamentals
Discussion: Introduction and syllabus overview
Lecture: What is data journalism and how does it occur in the wild?
Hands-on: what is data journalism according to you, find 4 other definitions, review of data stories.
At home:
Read the Introduction chapter of The Data Journalism Handbook.
1.2 — Week 2 (Feb. 6)
Understanding data structures, data types and exploring it with Google Sheets
Discussion: The professor presents an interesting data story or data set for the class to discuss
Lecture: Understanding data — a quick rundown of data structures, data types and data formats
Hands-on:
Installation party (Google Sheets edition!)
Google Sheets etiquette — you think you know Google Sheets but you don’t
At home:
Read “Chapter 2: a Newsroom math guide”, from “Numbers in the Newsroom: Using math and statistics in News”, 2nd Edition. By Sarah Cohen.
1.3 — Week 3 (Feb. 13)
Basic data crunching for journalists (part 1)
Discussion: Spencer presents an interesting data story or data set for the class to discuss
Lecture: Finding stories in your data
Hands-on: Exploring your data in Google Sheets (data types, formatting, sorting and filtering)
At home:
Homework 1: Import, clean and organize data with Google Sheets. Come up with a simple story for it
1.4 — Week 4 (Feb. 20)
Basic data crunching for journalists (part 2)
Discussion: Shira presents an interesting data story or data set for the class to discuss
Lecture: Data Sourcing: BLS data
Interactive lesson on data assignments: Students will come up with a news story based on the data headers they see based on the rubrics of the previous week
Hands-on: Get comfortable using math, formulas and functions to get answers from the BLS jobs report
- Math essentials (mean, median,percent change, percent difference vs percentage point difference, min, max, rates, per person averages)
-Pivot Tables advanced
- At home:
Homework 2 : Math quiz with Google Sheets
1.5— Week 5 (Feb. 27)
Census data and understanding data with visuals in Google Sheets**
**
Discussion: Pamela presents an interesting data story or data set for the class to discuss
Lecture: Data Sourcing: Census data
Hands-on: Understanding your data better with visuals
- Useful visualizations: conditional formatting, bar charts, column charts
**At home:
** Homework 3 : Visualizations in Google Sheets
1.6 — Week 6 (March 6)
Using Google Sheets and Open Refine to organize and clean messy data
Discussion: Lukas presents an interesting data story or data set for the class to discuss**
Lesson:** Why people suck — Data problems other people created for you and how you can solve them
Hands-on: Cleaning data with Tabula, Open Refine and Google Sheets
At home:
Homework 4 : Clean and analyze data with Open Refine and Sheets, then give us three bullet points with interesting findings
1.7 — Week 7 (March 13)
Merging two data sets**
**
Discussion: Jaime presents an interesting data story or data set for the class to discuss
Lecture: When two become one - the power of merging data sets
Hands-on: Data merging with Google Sheets (vlookups)
1.8 — Week 8 (March 20)
The benefits of raw data and how to get it via FOIL/FOIA
Discussion: Jo presents an interesting data story or data set for the class to discuss
Lecture: What is FOIA
Installation guide!
Hands-on: Write a FOIA request to the FTC
At home:
Homework 5: Send off your FOIA request and fill out your FOIA log
Finish installing python and libraries if you don’t finish in class**
**
2.1 — Week 9 (March 27)
Introduction to Python and the command line interface**
**
Discussion: Harsha presents an interesting data story or data set for the class to discuss
Lecture: Python in Journalism
Hands-on:
Introduction to Python basics
- The command-line tool, Python and the interactive shell
At home:
Homework 6: Python exercise
2.2 — Week 10 (April 3)
Data exploration with Jupyter Notebooks and Pandas
Discussion: Ron presents an interesting data story or data set for the class to discuss
Lecture: On assignment 1: Jobs report data reporting
Hands-on:
Your first Jupyter Notebook
Understand how to use Python and Jupyter Notebooks for most common calculations and analysis
Git and github
At home:
Homework 7: Python Jupyter Notebooks quiz
Refine and submit your assignment pitch by Monday April 8, 11:59PM
2.3 — Week 11 (April 10)
Data analysis with Pandas (sorting and filtering)
Discussion: Jacob presents an interesting data story or data set for the class to discuss
Lecture: On assignment 2: The Census Bureau
Hands-on: Jupyter Notebooks part 2
**- At home:
** Homework 8: Python Jupyter Notebooks data analysis
Refine and submit your assignment pitch by Friday April 12
2.4 — Week 12 (April 17)
Discussion: Virginia presents an interesting data story or data set for the class to discuss
Lecture: On assignment 3: Immigration data
Hands-on: Load options, Groupby, Lambdas and Functions
**At home:
** Refine and submit your assignment pitch by Friday April 19
- - - -
NO CLASS ON WEDNESDAY APRIL 24. SPRING BREAK.
- - - -
2.5— Week 13 (April 29)
Merging with Pandas
Discussion: Kelly presents an interesting data story or data set for the class to discuss
Lecture: On assignment 4: social data
Hands-on: Merging datasets with pandas
At home:
Refine and submit your assignment pitch by Friday May 3
2.6 — Week 14 (May 6)
**Geospatial data analysis with Geopandas
**
Discussion: Ethan presents an interesting data story or data set for the class to discuss
Lecture: Pick one of the past four pitches for a lightning talk at the end of next week. Refine them in class!
Hands-on: Bigfoot sightings exercise
Final class! Week 15 (May 13)
Pitch day, how did we all do?
Objective: Present your best pitch. Evaluate the course and instructors evaluation.
- Presentations
- Class evaluations
Class rules
Students-Instructors contract: The success of this course depends on the level of commitment of each student. That is, it is up to each student to carry out their class work and assignments as well as to contribute to their team’s reporting project and speak up about any doubts or concerns you may have. In return, the instructors will do their best to provide a clear lesson plan, give students timely feedback and advice them to achieve the course expected outcomes.
Attendance and punctuality: We meet 15 times during the spring semester. You must attend every class and be on time. If you’re sick or you have an emergency, let us know via Slack or text. If you don’t show up, you will hear from us. More than one unexcused absence will penalize your overall grade by 5%. Similarly, two tardiness equals an absence.
Deadlines matter. This is of vital importance, not only for the class but also in the professional career of any journalist, because deadlines are sacred. Please carefully note these rule: there will be a 10 percent deduction of your assignment grade for every 24 hours that passes after a deadline in which you have not turned it in. No exceptions. Except medical emergency or family emergency. Make-up work will not be offered except in extenuating circumstances.
Communicate. If you have a problem or if you have difficulties, tell us right away, not after is too late. In journalism that’s what we do. When we have a problem we immediately tell our editor.
Be accurate and use language correctly. The value of journalistic work depends on credibility. That is why class assignments must have a rigorous verification of the data and information presented. That is the basis of the profession. A story with erroneous information can carry out an F grade. We expect the language to be used correctly. Follow the AP Style guidelines. Aim for clarity, precision and correct spelling and grammar.
Keep up with the news. Consuming information on a daily basis leads to a healthy diet of background, helps you connect the dots and discover story ideas to work on. If you care about a topic or your subject concentration you have to stay in top of the game.
Be a pro. Honesty, courtesy, curiosity and professionalism are the core values of a journalist. Behave like one because you are a journalist. When classmates are presenting or we have guests or we are working in teams don’t multitask, focus.
Participation. It is important to maintain an attitude of openness. Class time is reserved for learning and discussing the topics of each session. It is not the time for personal calls, text messages, emails and social networks.
Diversity and inclusion. It's critical that students learn to include a diverse set of voices in their stories, something that is often glossed over when finding stories in spreadsheets and online sources. You are encouraged and expected to look for stories about and voices from communities that are underrepresented. This also applies to our classroom. It requires us all to discuss differences with respect and empathy, regarding race, gender, age, religion, sexual preference, disability, language, origin or political beliefs.
Code of honor. This class follows the guidelines of the Student Handbook of our school. More so, in journalism plagiarism or falsification of data, sources and facts are serious crimes that can lead to failing this class. You may also be the subject of suspension, probation or expulsion, pending the decision of the School administration.
Assignments and Due Dates
4 Story pitches: 80 points.
Each pitch should be 1 page long, 2 pages long max. and including the following things:
● Byline
● What is your story about? Tell us in 1 headline and 1 lead paragraph. This should include answers to the following questions:
○ Why this story is relevant ("So what?) and why now?
○ What is the single question your story tries to answer?
○ Why will this story resonate with your audience?
○ What else has been done on this topic? (Provide links) and how is your angle different or fresh?
● Show us your data work! Give us access to a Google Sheet or a Jupyter Notebook!
● Write up at least one or up to three findings from your analysis based on the dataset that was given
● Maximum/minimum.
○ What is the maximum (best) story possible?
○ What's the minimum (fallback) story if your hypothesis doesn't prove out?
Class participation, readings and homework: 20 points
We expect you to participate in the class and pay respect to each other. This means partaking in discussions, in the hands-on drills and other class activities as well as completing homework.
The 8 homework assignments are individual drills to to evaluate your understanding of the material taught in classes.
- Total: 100 points.
How to file assignments
Each student will create a personal Drive homework folder name-lastname-homework to save his/her homework. Each team will create a team folder team-lastname-lastname-lastname to file teamwork. After you create your personal and team folder, share them with your instructors with editing permission.
All assignments are filed by class day at noon, by saving them to your drive folder (individual homework folder, and team folder) and by recording that you filed in the assignments form: http://bit.ly/fileheredj18
Breakdown of due dates
Details for each homework assignment will be posted in the shared Google Classroom. Details of the requirements for team reporting assignments are already posted in Google Classroom and will be explained in the first class.
Grading
Rubric
The story pitches makes up 80**% of your grade, each 20%. Graded as:**
4 Pitches: 80 points altogether, 20 points each
Each pitch
20 pts. On time + meets all project criteria + original reporting + effective use of data
17 pts. On time + meets most if not all of project criteria + acceptable reporting + acceptable use of data
13 pts. On time + meets very little of project criteria + somewhat acceptable reporting + somewhat acceptable use of data
10 pts. Late and/or meets little of project criteria + weak reporting + weak use of data
7 pts. Late and/or does not mean project criteria + very weak reporting + very weak use of data
3 pts. Late and/or shows little to no effort
0 pts. Not submitted within 1 week of deadline
8 Homeworks: 16 points
Each homework
2 pts. Completed homework.
1 pts. Completed partially. If this happens, your instructor will leave you a short comment to help you complete the exercise. If you do it, you’ll get full points.
0 pts. Not submitted
Class participation and readings discussion. 4 points.
4 pts. Amazing participation, asks questions, comments readings, shares ideas, works well with others
3 pts. Most of the time participates, asks questions, shares ideas, works well with others.
2 pts. Could do better
1 pts. Not engaged most of the times
0 pts. Not engaged at all
Total points: 100 = 100%
Scale
Final course grades, according to the grading scale used in the CUNY Graduate School of Journalism:
● 97: A+ Stellar work. Ready to be published by a professional news organization with minimal changes.
● 93: A Excellent work. It is ready to be published professionally with some changes.
● 90: A- Good quality work, although it needs a slightly more significant revision to be able to be published.
● 87: B+ Solid work that shows some deficiencies that need to be solved.
● 83: B Meets certain requirements, but lacks several important elements.
● 80: B- Below average and needs strong overall improvements.
● 77: C+ Poor job. It presents many problems of structure, reporting and storytelling
● 73: C Almost unacceptable because of major overall problems.
● 70: C- Unacceptable. Does not meet the minimum requirements of a graduate level journalism project.
● Anything below a 70 is an F. Work has failed at every level. There are no D in CUNY’s grading scale.
Guides and tipsheets
● Research Guides for Reporters by The Newmark J-School Research Center
● Data-driven story resources by The Newmark J-School Research Center
● Tips for doing data stories by Miguel Paz
● The Quartz guide to bad data, by Christopher Groskopf & Quartz GitHub Contributors
● A Guide to Bulletproofing Your Data by ProPublica
● Tipsheet: Most common data formats and concepts, compiled by Miguel Paz
● Data is plural, curated list of useful data, compiled by Jeremy Singer-Vine from Buzzfeed (sign up for updates)
● The Quartz Directory of Essential Data, by Christopher Groskopf
● First Draft News verification resources
● The Verification Handbook, European Journalism Centre, edited by Craig Silverman
● Fact checking guides, Open News
● Finding Stories in Census Data, by Emily Alpert Reyes
● How to Use the Census Bureau’s American Community Survey like a Pro, by Paul Overberg
● Pushing Hot Buttons with Census.gov: Using census data to find facts in a world of speculation, by Ronald Campbell
● Understanding Households and Relationships in Census Data, by Anthony DeBarros
Further reading
Your class readings will be provided in class. Find some more recommended books here:
● “Numbers in the Newsroom: Using math and statistics in News”, 2nd Edition. By Sarah Cohen.
● “The investigative reporter's handbook: a guide to documents, databases, and techniques”. 4th Edition. Edited by Brant Houston et al.
● “Computer-Assisted Reporting: A practical guide”, 4th Edition. By Brant Houston.
● “Precision Journalism: a Reporter’s Introduction to Social Science Methods”, 4th Edition. By Philip Meyer.
● “The Functional Art: An introduction to information graphics and visualization”. By Alberto Cairo.
● “The Curious Journalist Guide to Data” (online). By Jonathan Stray.
● “Storytelling with Data”. By Cole Nussbaumer Knaflic
● “Computer-Assisted Research: Information Strategies and Tools for Journalists”. By Nora Paul and Kathleen A. Hansen
● “Mapping for Stories: A Computer-Assisted Reporting Guide”. By Jennifer LaFleur and Andy Lehren
● “The Visual Display of Quantitative Information”. By Edward R. Tufte
● “Data Points: Visualization That Means Something”. By Nathan Yau
● “Design for Information”. By Isabel Meirelles
Instructors will also share tip sheets, stories and tutorials for specific lessons.
Coaches
You'll find all the coaches here.
Most relevant to our class:
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